VentrEX : An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI

VentrEX : An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article VentrEX : An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI Abla Bedoui, Julieta Anahi Rancati, Ignacio Lugones, Mohammed Cherkaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7836207/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background : Automated segmentation of the left and right ventricles (LV) and (RV) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift. Methods : We present VentrEX, an anatomically guided pipeline. The core segmenter, VentrEX-Seg is a 3D encoder-decoder with {parallel channel-spatial attention} and a {Transformer} bottleneck. Training is performed exclusively on ACDC. A lightweight PM/T module automatically extracts papillary and trabecular burden and standardizes cavity volumes. External evaluation is {zero-shot} (no fine-tuning) on Sunnybrook (LV) and MM-WHS MRI (RV). We report Dice, HD95 (mm); for volumetry we use Bland-Altman analyses (LV and RV volumes). Attention/Grad-CAM visualizations support interpretability. Results : On ACDC, VentrEX achieved higher Dice and lower boundary error than U-Net, nnU-Net, CBAM, and VentrEX-Seg. Zero-shot performance was preserved externally (e.g., Sunnybrook LV Dice~0.9053, HD95~4.95\,mm; MM-WHS RV Dice~0.9236, HD95~6.61\,mm). PM/T standardization reduced volumetric bias and narrowed limits of agreement in Bland-Altman analyses. Qualitative overlays and 3D reconstructions showed fewer PM/T "leaks" and anatomically plausible borders across ED/ES. Conclusions : Single-source training with dual zero-shot external tests demonstrates robustness under domain shift, while explicit PM/T modeling reduces volume bias and improves reproducible volumetry. The combination of parallel attention and a Transformer bottleneck enables accurate, transparent cine-CMR segmentation across datasets. Cardiac MRI Deep learning Transformer Ventricular segmentation Papillary muscles Trabeculae Explainable AI Domain shift Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 13 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Editor invited by journal 16 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7836207","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531511352,"identity":"d7863c36-ea0f-4473-a26d-1e7599077f84","order_by":0,"name":"Abla 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MRI","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac MRI, Deep learning, Transformer, Ventricular segmentation, Papillary muscles, Trabeculae, Explainable AI, Domain shift","lastPublishedDoi":"10.21203/rs.3.rs-7836207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7836207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u0026nbsp; Automated segmentation of the left and right ventricles (LV) and (RV) in cine cardiac MRI (CMR) underpins reliable volumetry and mass estimation. However, papillary muscles and trabeculae (PM/T) introduce clinically meaningful variability and exacerbate cross-dataset domain shift.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We present VentrEX, an anatomically guided pipeline. The core segmenter, VentrEX-Seg is a 3D encoder-decoder with {parallel channel-spatial attention} and a {Transformer} bottleneck. Training is performed exclusively on ACDC. A lightweight PM/T module automatically extracts papillary and trabecular burden and standardizes cavity volumes. External evaluation is {zero-shot} (no fine-tuning) on Sunnybrook (LV) and MM-WHS MRI (RV). We report Dice, HD95 (mm); for volumetry we use Bland-Altman analyses (LV and RV volumes). Attention/Grad-CAM visualizations support interpretability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: On ACDC, VentrEX achieved higher Dice and lower boundary error than U-Net, nnU-Net, CBAM, and VentrEX-Seg. Zero-shot performance was preserved externally (e.g., Sunnybrook LV Dice~0.9053, HD95~4.95\\,mm; MM-WHS RV Dice~0.9236, HD95~6.61\\,mm). PM/T standardization reduced volumetric bias and narrowed limits of agreement in Bland-Altman analyses. Qualitative overlays and 3D reconstructions showed fewer PM/T \"leaks\" and anatomically plausible borders across ED/ES.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Single-source training with dual zero-shot external tests demonstrates robustness under domain shift, while explicit PM/T modeling reduces volume bias and improves reproducible volumetry. The combination of parallel attention and a Transformer bottleneck enables accurate, transparent cine-CMR segmentation across datasets.\u003c/p\u003e","manuscriptTitle":"VentrEX : An Anatomically Guided Deep Learning Pipeline for Ventricular Segmentation in Cine Cardiac MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 03:13:14","doi":"10.21203/rs.3.rs-7836207/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"331635424923181907415124717642240462352","date":"2025-11-13T13:39:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-13T11:41:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T06:49:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-16T06:22:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T22:47:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-10-15T22:44:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5cde1b58-68c7-48a2-818d-a78e1f1c7727","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T11:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 03:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7836207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7836207","identity":"rs-7836207","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}